Multi-task learning using variational auto-encoder for sentiment classification.

• We design a hybrid structure neural network (MTVAE) for sentiment classification. • We combine with generative model, five-point classification and binary classification for training simultaneously. • Experimental results show that our multi-task learning model outperforms most of state-of-the-art approaches. With the rapid growth of the big data, many approaches in the representation of text for sentiment classification have been successfully proposed in natural language processing. However, these approaches remedy this problem based on single-task supervised objectives learning and do not consider their relative of multiple tasks. Based on these defects, in this work, we consider these tasks are relative, and use weight-shared parameters for learning the representation of text in neural network model, we introduce and study a multi-task approach with variational auto-encoder generative model (MTVAE) by jointly learning them. Experimental results on six subsets of Amazon review data show that the proposed approach can effectively improve the sentiment classification accuracy by other relative tasks. [ABSTRACT FROM AUTHOR]